GLC: A dual-perspective approach for identifying influential nodes in complex networks

Identifying influential spreaders is crucial for understanding the dynamics of information diffusion within complex networks. Several centrality methods have been proposed to address this, but these studies often concentrate on only one aspect. To solve this problem, we introduce a dual-perspective...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Expert systems with applications Ročník 268; s. 126292
Hlavní autori: Ruan, Yirun, Liu, Sizheng, Tang, Jun, Guo, Yanming, Yu, Tianyuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 05.04.2025
Predmet:
Cluster algorithm
Complex network
Global critical nodes
Local influence
Node influence
Cluster algorithm
Local influence
Global critical nodes
Node influence
Complex network
ISSN:0957-4174
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Identifying influential spreaders is crucial for understanding the dynamics of information diffusion within complex networks. Several centrality methods have been proposed to address this, but these studies often concentrate on only one aspect. To solve this problem, we introduce a dual-perspective approach which considers both global and local perspectives for identifying influential nodes in complex networks. From a global perspective, if a node has the capability to efficiently transmit information to various clusters within a network, then the information originating from that node will quickly spread across a large area. From a local perspective, when a node has a greater number of neighbors—especially those that are significant within the network—the information emanating from that node is less likely to be confined to a localized region. Based on this understanding, we first design a novel clustering method to detect groups in which the connections among nodes are denser than those with the rest of the network. The most influential nodes in each group are identified as global critical nodes. Subsequently, the local influence of a node is defined by the number and significance of its neighboring nodes. Ultimately, nodes are ranked according to their local influence, their proximity to the global critical nodes using the shortest paths, and the importance of these global critical nodes. To evaluate the performance of the proposed method, the susceptible-infected-removed (SIR) diffusion model is used. Results of the investigation on real networks and realistic synthetic benchmarks show that the proposed method can identify nodes with high influence better than other centrality methods.
ISSN:0957-4174
DOI:10.1016/j.eswa.2024.126292